Bailey et al., 1995 highlighted the transitory nature of children's physical activity using a very comprehensive observation protocol with fifteen American 6-10 year-old children. This study recorded the frequency, duration and varying intervals between activities of different intensity and duration (tempo). The protocol was very labour-intensive; observation periods were four hours in duration and observers were cued every three seconds to record activity by an audible bleep from a microcassette recorder earphone. The median duration of low and medium intensity activities was found to be a mere 6 s, and for high intensity activities it was only 3 s. Re-analysis of the same data set with spectral analysis showed 83 ± 11 bouts of activity per hour in boys and 89 ± 12 bouts per hour in girls and revealed the mean bout duration was 21 ± 5 s in boys and 20 ± 4 s in girls (Berman et al., 1998).

More recently, researchers have used high-frequency accelerometry monitoring to assess the temporal pattern of children's activity. Despite the different methodologies, results are very similar to those reported from the observation protocols. Baquet et al., 2007 reported a mean bout duration of 22.1 ± 3.5 s in French 8-10 year-olds and showed that 80, 93 and 96% of activity bouts of moderate, vigorous and very high intensity, respectively, were shorter than 10 s. Although the proportion of time spent in vigorous and very high intensity activity was low (<3%), it accounted for over a third of the total physical activity (Baquet et al., 2007). This highlights the importance of accurately quantifying these short bouts of intense activity. Many methods of activity measurement are fairly insensitive to these short bouts. The lack of appreciation of and quantification of the transitory nature of the activity patterns in children may have affected the ability of studies to provide an accurate reflection of children's activity and this will have impacted on the determination of associations between activity and health.

Heart rate is not a direct measure of physical activity. However, it does provide an indication of the relative stress placed upon the cardiopulmonary system by physical activity (Armstrong, 1998). Heart rate monitoring also allows the recording of values over time, which facilitates a visual assessment of the pattern and intensity of activity. It was the first widely used objective measure of physical activity in children. In the South West of England alone, over a ten year period Neil Armstrong and colleagues monitored the heart rates of over 1200 5-16 year-old children over three schooldays (Armstrong et al., 1990; 2000; Armstrong and Bray, 1990; 1991; Biddle et al., 1991; McManus and Armstrong, 1995; Welsman and Armstrong, 1997; 1998; 2000).

However, there are a number of limitations to the use of heart rate monitoring for assessing physical activity (Armstrong and Welsman, 2006; Rowlands, 2001; Rowlands et al., 1997). Physical activity is not the only factor that causes changes in heart rate. Heart rate can also be influenced by other parameters, e.g. emotional stress, anxiety, level of fitness, type of muscular contraction, active muscle group, hydration and environment (Armstrong and Welsman, 2006; Rowlands et al., 1997). These factors can have the greatest influence at low activity intensity; hence Riddoch and Boreham, 1995 recommended that heart rate monitoring should be considered primarily as a tool for the assessment of moderate to vigorous activity and that heart rates below 120 bpm would not normally be considered to be valid estimates of physical activity.

The methods of analysing heart rate activity data are numerous with over 24 different methods of analysing heart rate data identified by Harro and Riddoch, 2000, This diversity in methods of analysis limits the comparability between studies. However, most studies predict energy expenditure from individual or group regression equations, report the time spent above pre-determined heart rate thresholds or report net heart rate (heart rate minus resting heart rate). To account for the problems associated with prediction of energy expenditure from low heart rates, investigators have used a 'flex' heart rate approach, whereby heart rates below a pre-determined threshold are considered to equate to resting energy expenditure and an individual heart rate: energy expenditure regression equation is used to predict energy expenditure above this threshold (Livingstone et al., 1992). However, the labour intensive nature of individual calibration limits the use of this technique (Armstrong and Welsman, 2006) and the majority of studies report time spent above heart rate thresholds equivalent to moderate and vigorous activity.

The method chosen for the analysis of heart rate data needs careful consideration as it can affect the interpretation of the data and hence conclusions regarding habitual activity status and associations with health variables. For example, Janz et al., 1992 reported a negative relationship between children's activity and fatness when expressing heart rate as net heart rate, whereas when expressing the same heart rate data as time spent above intensity thresholds the relationship was weaker and non-significant. A meta-analysis of fifty studies investigating the relationship between activity and body fat in children and youth showed that the average effect size elicited from studies using heart rate to assess activity levels, most of which used time spent above moderate and vigorous thresholds, was significantly lower than the average effect size elicited from studies using a different method of activity assessment (questionnaire, observation, motion counter) (Rowlands et al., 2000). It is possible that increased body fatness increases the cardiovascular stress, and hence heart rate, during normal activities (Rowlands et al., 1999).

Heart rate monitors are generally set to store heart rate values every minute. However, as suggested by Armstrong and Welsman, 2006, in order to capture the rapid short bursts of activity characteristic of children's activity, a smaller sampling interval would be optimal. As the heart rate response tends to lag behind changes in movement (Rowlands et al., 1997) and there is a rapid transition between activities associated with children's behaviour (Bailey et al., 1995), it is unlikely the heart rate response would be able to provide a comprehensive picture of the temporal pattern of activity typical of children.

Despite the above limitations, heart rate monitoring has provided a valid and reliable objective estimate of physical activity, particularly sustained periods of moderate and vigorous activity, and the vast quantity of data collected has given researchers valuable, objective insights into the nature of children's activity over the past twenty years.

Leonardo da Vinci designed the pedometer approximately 500 years ago (Gibbs-Smith, 1978); it is a simple mechanical motion sensor that records the acceleration and deceleration of movement in one direction. Generally, the pedometer gives a measure of total activity, or movements, over the time period assessed, although more sophisticated models are available. The well-documented disadvantages of this method include the pedometer's inability to measure intensity, record counts during cycling and record increases in energy expenditure due to carrying objects or walking/running uphill (Rowlands, 2001; Rowlands et al., 1997).

Early studies using mechanical pedometers concluded that they were inaccurate at counting steps or measuring distance walked (Gayle et al., 1977; Kemper and Verschuur, 1977; Saris and Binkhorst, 1977; Washburn et al., 1980). However, during the last ten to fifteen years, studies have provided evidence for the reliability and validity of electronic pedometers for the quantification of distance walked, number of steps taken (Bassett et al., 1996), assessment of total daily activity (Sequeira et al., 1995) and estimation of activity intensity and duration (Tudor-Locke et al., 2005; Rowlands and Eston, 2005). Reliability and validity do differ by brand, hence it is important to consult some of the comparative studies (e.g. Schneider et al., 2004; Tudor-Locke et al., 2006) and test the accuracy of the pedometers with the population of interest before commencing a study.

Kilanowski et al., 1999 investigated the validity of pedometry as a measure of daily activity of 10-12 year-old children using contemporaneous measures of pedometry (Yamax Digi-walker SW-200, Yamasa, Tokyo, Japan), triaxial accelerometry (Tritrac Professional Products, Reining International, Madison, WI, USA) and observation. Pedometer counts correlated significantly with both observation and triaxial accelerometry counts during high intensity and low intensity recreational activities. In the same year, a study from our laboratory showed that activity measured by pedometry or the Tritrac triaxial accelerometer, correlated positively with fitness (Tritrac r = 0.66; Pedometer = 0.59, p < 0.01) and negatively with fatness (Tritrac r = -0.42; Pedometer = -0.42 p < 0.05) in 34 boys and girls, aged 8-10 years (Rowlands et al., 1999). It is notable that the simple pedometer identified the same relationships with fitness and fatness as the relatively sophisticated Tritrac. In contrast, contemporaneous measures of time spent above moderate and vigorous heart rate thresholds were not related to body fatness.

Over the last ten years, there has been a growth in the number of studies that have used pedometry to assess physical activity in children. The method is objective, cheap, unobtrusive and ideal for large population surveys, or any situation where only a measure of total activity and not activity pattern is required. Recent studies have shown positive relationships between children's daily step counts and aerobic fitness (Le Masurier and Corbin, 2006), bone density (Rowlands et al., 2002), psychological well-being (Parfitt and Eston, 2005) and negative relationships with body fatness (Duncan et al., 2006).

There is a possibility that the act of wearing any activity monitor will cause a child to engage in reactive behaviour. This is defined as "a change in normal activity levels because of the participants' knowledge that their activity levels are being monitored" (Welk et al., 2000, p.59). The likelihood of reactive behaviour is potentially greater when activity is assessed using pedometers as children may be aware of their pedometer scores and/or able to check their score throughout the course of the day. This has led many researchers to 'blind' children to their scores by sealing the pedometers. The output can then be collated in a number of different ways. At the most controlled level, researchers may visit school each day and take pedometer scores from each child, re-sealing the pedometer after it has been read. However, this is a problem at weekends, so some researchers have provided one pedometer for each day of the measurement (well marked) and the child simply wears the appropriate pedometer each day and hands all the pedometers in at the end of the study. Alternatively, protocols may require parents/guardians to read the pedometer scores and re-seal the pedometer after the child has gone to bed. Other protocols make no attempt to blind the child to the pedometer output. Research has indicated little evidence for reactive behaviours whether the child is blinded to the pedometer output (Vincent and Pangrazi, 2002) or not (Ozdoba et al., 2004). We have assessed the difference between sealed and unsealed pedometers worn simultaneously by 9-11 year-old children and found no consistent discrepancy between the pedometers (unpublished data). It appears that valid measures of daily habitual activity can be obtained from sealed and unsealed pedometers, however researchers may wish to seal pedometers during the day to minimise the risk of the pedometer accidentally being re-set and the loss of the days' data.

The pedometer also holds promise as a motivational tool to self-regulate physical activity levels. Pedometer-based intervention studies have demonstrated increased steps/day using set or individualised goals in adults (e.g. Chan et al., 2004; Tudor-Locke et al., 2004). Studies with children show that rewards based on access to television viewing combined with pedometer-based goals are effective in increasing children's activity levels (Goldfield et al., 2000; Roemmich et al., 2004), but that pedometer-based goals alone, without the rewards, are not as effective (Goldfield et al., 2006). We have shown that a peer-modelling, rewards (small customised toys, e.g. balls and frisbees) and pedometer-feedback intervention was successful in increasing physical activity in 9-11 year-old children (unpublished data). Therefore, evidence exists for the use of a pedometer not only as a measurement tool, but also as an intervention tool for behaviour change.